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AI has already changed weather forecasting forever.
It’s been a wild few years in the typically tedious world of weather predictions. For decades, forecasts have been improving at a slow and steady pace — the standard metric is that every decade of development leads to a one-day improvement in lead time. So today, our four-day forecasts are about as accurate as a one-day forecast was 30 years ago. Whoop-de-do.
Now thanks to advances in (you guessed it) artificial intelligence, things are moving much more rapidly. AI-based weather models from tech giants such as Google DeepMind, Huawei, and Nvidia are now consistently beating the standard physics-based models for the first time. And it’s not just the big names getting into the game — earlier this year, the 27-person team at Palo Alto-based startup Windborne one-upped DeepMind to become the world’s most accurate weather forecaster.
“What we’ve seen for some metrics is just the deployment of an AI-based emulator can gain us a day in lead time relative to traditional models,” Daryl Kleist, who works on weather model development at the National Oceanic and Atmospheric Administration, told me. That is, today’s two-day forecast could be as accurate as last year’s one-day forecast.
All weather models start by taking in data about current weather conditions. But from there, how they make predictions varies wildly. Traditional weather models like the ones NOAA and the European Centre for Medium-Range Weather Forecasts use rely on complex atmospheric equations based on the laws of physics to predict future weather patterns. AI models, on the other hand, are trained on decades of prior weather data, using the past to predict what will come next.
Kleist told me he certainly saw AI-based weather forecasting coming, but the speed at which it’s arriving and the degree to which these models are improving has been head-spinning. “There's papers coming out in preprints almost on a bi-weekly basis. And the amount of skill they've been able to gain by fine tuning these things and taking it a step further has been shocking, frankly,” he told me.
So what changed? As the world has seen with the advent of large language models like ChatGPT, AI architecture has gotten much more powerful, period. The weather models themselves are also in a cycle of continuous improvement — as more open source weather data becomes available, models can be retrained. Plus, the cost of computing power has come way down, making it possible for a small company like Windborne to train its industry-leading model.
Founded by a team of Stanford students and graduates in 2019, Windborne used off-the-shelf Nvidia gaming GPUs to train its AI model, called WeatherMesh — something the company’s CEO and co-founder, John Dean, told me wouldn’t have been possible five years ago. The company also operates its own fleet of advanced weather balloons, which gather data from traditionally difficult-to-access areas.
Standard weather balloons without onboard navigation typically ascend too high, overinflate, and pop within a matter of hours (thus becoming environmental waste, sad!). Since it’s expensive to do launches at sea or in areas without much infrastructure, there’s vast expanses of the globe where most balloons aren’t gathering any data at all.
Satellites can help, of course. But because they’re so far away, they can’t provide the same degree of fidelity. With modern electronics, though, Windborne found it could create a balloon that autonomously changes altitude and navigates to its intended target by venting gas to descend and dropping ballast to ascend.
“We basically took a lot of the innovations that lead to smartphones, global satellite communications, all of the last 20 years of progress in consumer electronics and other things and applied that to balloons,” Dean told me. In the past, the electronics needed to control Windborne’s system would have been too heavy — the balloon wouldn’t have gotten off the ground. But with today’s tiny tech, they can stay aloft for up to 40 days. Eventually, the company aims to recover and reuse at least 80% of its balloons.
The longer airtime allows Windborne to do more with less. While globally there are more than 1,000 conventional weather balloons launched every day, Dean told me, “We collect roughly on the order of 10% or 20% of the data that NOAA collects every day with only 100 launches per month.” In fact, NOAA is a customer of the startup — Windborne already makes millions in revenue selling its weather balloon data to various government agencies.
Now, with a potentially historic hurricane season ramping up, Windborne has the potential to provide the most accurate data on when and where a storm will touch down.
Earlier this year, the company used WeatherMesh to run a case study on Hurricane Ian, the Category 5 storm that hit Florida in September 2022, leading to over 150 fatalities and $112 billion in damages. Using only weather data that was publicly available at the time, the company looked at how accurately its model (had it existed back then) would have tracked the hurricane.
Very accurately, it turns out. Windborne’s predictions aligned neatly with the storm’s actual path, while the National Weather Service’s model was off by hundreds of kilometers. That impressed Khosla Ventures, which led the company’s $15 million Series A funding round earlier this month. “We haven’t seen meaningful innovation in weather since The Weather Channel in the 90s. Yet it’s a $100 billion market that touches essentially every industry,” Sven Strohband, a partner and managing director at Khosla Ventures, told me via email.
With this new funding, Windborne is scaling up its fleet of balloons as it prepares to commercialize. The money will also help Windborne advance its forecasting model, though Dean told me robust data collection is ultimately what will set the company apart. “In any kind of AI industry, whoever has the top benchmark at any given time, it’s going to fluctuate,” Dean said. “What matters is the model plus the unique datasets.”
Unlike Windborne, the tech giants with AI-based weather models — including, most recently, Microsoft — aren’t gathering their own data, instead drawing solely on publicly accessible information from legacy weather agencies.
But these agencies are starting to get into the game, too. The European Centre for Medium-Range Weather Forecasts has already created its own AI-based model, the Artificial Intelligence/Integrated Forecasting System, which it runs in parallel to its traditional model. NOAA, while a bit behind, is also looking to follow suit.
“In the end, we know we can't rely on these big tech companies to just keep developing stuff in good faith to give to us for free,” Kleist told me. Right now, many of the top AI-based weather models are open source. But who knows if that will last? “It's our mission to save lives and property. And we have to figure out how to do some of this development and operationalize it from our side, ourselves,” Kleist said, explaining that NOAA is currently prototyping some of its own AI-based models.
All of these agencies are in the early stages of AI modeling, which is why you likely haven’t noticed weather predictions making a pronounced leap in accuracy as of late. It’s all still considered quite experimental. “Physical models, the pro is we know the underlying assumptions we make. We understand them. We have decades of history of developing them and using them in operational settings,” Kleist told me. AI-based models are much more of a black box, and there’s questions surrounding how well they will perform when it comes to predicting rare weather events, for which there might be little to no historical data for the model to reference.
That hesitation might not last long, though. “To me it’s fairly obvious that most of the forecasts that would actually be used by users in the future will come from machine learning models,” Peter Dueben, head of Earth systems modeling at the European Centre for Medium Range Weather Forecasting, told me. “If you just want to get the weather forecast for the temperature in California tomorrow, then the machine learning model is typically the better choice,” he added.
That increased accuracy is going to matter a lot, not just for the average weather watcher, but also for specific industries and interest groups for whom precise predictions are paramount. “We can tailor the actual models to particular sectors, whether it's agriculture, energy, transportation,” Kleist told me, “and come up with information that's going to be at a very granular, specific level to a particular interest.” Think grid operators or renewable power generators who need to forecast demand or farmers trying to figure out the best time to irrigate their fields or harvest crops.
A major (and perhaps surprising) reason this type of customization is so easy is because once AI-based weather models are trained, they’re actually orders of magnitude cheaper and less computationally intensive to run than traditional models. All of this means, Kleist told me, that AI-based weather models are “going to be fundamentally foundational for what we do in the future, and will open up avenues to things we couldn't have imagined using our current physical-based modeling.”
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A new Data for Progress poll provided exclusively to Heatmap shows steep declines in support for the CEO and his business.
Nearly half of likely U.S. voters say that Elon Musk’s behavior has made them less likely to buy or lease a Tesla, a much higher figure than similar polls have found in the past, according to a new Data for Progress poll provided exclusively to Heatmap.
The new poll, which surveyed a national sample of voters over the President’s Day weekend, shows a deteriorating public relations situation for Musk, who has become one of the most powerful individuals in President Donald Trump’s new administration.
Exactly half of likely voters now hold an unfavorable view of Musk, a significant increase since Trump’s election. Democrats and independents are particularly sour on the Tesla CEO, with 81% of Democrats and 51% of independents reporting unfavorable views.
By comparison, 42% of likely voters — and 71% of Republicans — report a favorable opinion of Musk. The billionaire is now eight points underwater with Americans, with 39% of likely voters reporting “very” unfavorable views. Musk is much more unpopular than President Donald Trump, who is only about 1.5 points underwater in FiveThirtyEight’s national polling average.
Perhaps more ominous for Musk is that many Americans seem to be turning away from Tesla, the EV manufacturer he leads. About 45% of likely U.S. voters say that they are less likely to buy or lease a Tesla because of Musk, according to the new poll.
That rejection is concentrated among Democrats and independents, who make up an overwhelming share of EV buyers in America. Two-thirds of Democrats now say that Musk has made them less likely to buy a Tesla, with the vast majority of that group saying they are “much less likely” to do so. Half of independents report that Musk has turned them off Teslas. Some 21% of Democrats and 38% of independents say that Musk hasn’t affected their Tesla buying decision one way or the other.
Republicans, who account for a much smaller share of the EV market, do not seem to be rushing in to fill the gap. More than half of Republicans, or 55%, say that Musk has had no impact on their decision to buy or lease a Tesla. While 23% of Republicans say that Musk has made them more likely to buy a Tesla, roughly the same share — 22% — say that he has made them less likely.
Tesla is the world’s most valuable automaker, worth more than the next dozen or so largest automakers combined. Musk’s stake in the company makes up more than a third of his wealth, according to Bloomberg.
Thanks in part to its aging vehicle line-up, Tesla’s total sales fell last year for the first time ever, although it reported record deliveries in the fourth quarter. The United States was Tesla’s largest market by revenue in 2024.
Musk hasn’t always been such a potential drag on Tesla’s reach. In February 2023, soon after Musk’s purchase of Twitter, Heatmap asked U.S. adults whether the billionaire had made them more or less likely to buy or lease a Tesla. Only about 29% of Americans reported that Musk had made them less likely, while 26% said that he made them more likely.
When Heatmap asked the question again in November 2023, the results did not change. The same 29% of U.S. adults said that Musk had made them less likely to buy a Tesla.
By comparison, 45% of likely U.S. voters now say that Musk makes them less likely to get a Tesla, and only 17% say that he has made them more likely to do so. (Note that this new result isn’t perfectly comparable with the old surveys, because while the new poll surveyed likely voters , the 2023 surveys asked all U.S. adults.)
Musk’s popularity has also tumbled in that time. As recently as September, Musk was eight points above water in Data for Progress’ polling of likely U.S. voters.
Since then, Musk has become a power player in Republican politics and been made de facto leader of the Department of Government Efficiency. He has overseen thousands of layoffs and sought to win access to computer networks at many federal agencies, including the Department of Energy, the Social Security Administration, and the IRS, leading some longtime officials to resign in protest.
Today, he is eight points underwater — a 16-point drop in five months.
“We definitely have seen a decline, which I think has mirrored other pollsters out there who have been asking this question, especially post-election,” Data for Progress spokesperson Abby Springs, told me .
The new Data for Progress poll surveyed more than 1,200 likely voters around the country on Friday, February 14, and Saturday, February 15. Its results were weighted by demographics, geography, and recalled presidential vote. The margin of error was 3 percentage points.
On Washington walk-outs, Climeworks, and HSBC’s net-zero goals
Current conditions: Severe storms in South Africa spawned a tornado that damaged hundreds of homes • Snow is falling on parts of Kentucky and Tennessee still recovering from recent deadly floods • It is minus 39 degrees Fahrenheit today in Bismarck, North Dakota, which breaks a daily record set back in 1910.
Denise Cheung, Washington’s top federal prosecutor, resigned yesterday after refusing the Trump administratin’s instructions to open a grand jury investigation of climate grants issued by the Environmental Protection Agency during the Biden administration. Last week EPA Administrator Lee Zeldin announced that the agency would be seeking to revoke $20 billion worth of grants issued to nonprofits through the Greenhouse Gas Reduction Fund for climate mitigation and adaptation initiatives, suggesting that the distribution of this money was rushed and wasteful of taxpayer dollars. In her resignation letter, Cheung said she didn’t believe there was enough evidence to support grand jury subpoenas.
Failed battery maker Northvolt will sell its industrial battery unit to Scania, a Swedish truckmaker. The company launched in 2016 and became Europe’s biggest and best-funded battery startup. But mismanagement, production delays, overreliance on Chinese equipment, and other issues led to its collapse. It filed for Chapter 11 bankruptcy protection in November and its CEO resigned. As Reutersreported, Northvolt’s industrial battery business was “one of its few profitable units,” and Scania was a customer. A spokesperson said the acquisition “will provide access to a highly skilled and experienced team and a strong portfolio of battery systems … for industrial segments, such as construction and mining, complementing Scania's current customer offering.”
TikTok is partnering with Climeworks to remove 5,100 tons of carbon dioxide from the air through 2030, the companies announced today. The short-video platform’s head of sustainability, Ian Gill, said the company had considered several carbon removal providers, but that “Climeworks provided a solution that meets our highest standards and aligns perfectly with our sustainability strategy as we work toward carbon neutrality by 2030.” The swiss carbon capture startup will rely on direct air capture technology, biochar, and reforestation for the removal. In a statement, Climeworks also announced a smaller partnership with a UK-based distillery, and said the deals “highlight the growing demand for carbon removal solutions across different industries.”
HSBC, Europe’s biggest bank, is abandoning its 2030 net-zero goal and pushing it back by 20 years. The 2030 target was for the bank’s own operations, travel, and supply chain, which, as The Guardiannoted, is “arguably a much easier goal than cutting the emissions of its loan portfolio and client base.” But in its annual report, HSBC said it’s been harder than expected to decarbonize supply chains, forcing it to reconsider. Back in October the bank removed its chief sustainability officer role from the executive board, which sparked concerns that it would walk back on its climate commitments. It’s also reviewing emissions targets linked to loans, and considering weakening the environmental goals in its CEO’s pay package.
A group of 27 research teams has been given £81 million (about $102 million) to look for signs of two key climate change tipping points and create an “early warning system” for the world. The tipping points in focus are the collapse of the Greenland ice sheet, and the collapse of north Atlantic ocean currents. The program, funded by the UK’s Advanced Research and Invention Agency, will last for five years. Researchers will use a variety of monitoring and measuring methods, from seismic instruments to artificial intelligence. “The fantastic range of teams tackling this challenge from different angles, yet working together in a coordinated fashion, makes this program a unique opportunity,” said Dr. Reinhard Schiemann, a climate scientist at the University of Reading.
In 2024, China alone invested almost as much in clean energy technologies as the entire world did in fossil fuels.
Editor’s note: This story has been updated to correct the name of the person serving as EPA administrator.
Rob and Jesse get real on energy prices with PowerLines’ Charles Hua.
The most important energy regulators in the United States aren’t all in the federal government. Each state has its own public utility commission, a set of elected or appointed officials who regulate local power companies. This set of 200 individuals wield an enormous amount of power — they oversee 1% of U.S. GDP — but they’re often outmatched by local utility lobbyists and overlooked in discussions from climate advocates.
Charles Hua wants to change that. He is the founder and executive director of PowerLines, a new nonprofit engaging with America’s public utility commissions about how to deliver economic growth while keeping electricity rates — and greenhouse gas emissions — low. Charles previously advised the U.S. Department of Energy on developing its grid modernization strategy and analyzed energy policy for the Lawrence Berkeley National Laboratory.
On this week’s episode of Shift Key, Rob and Jesse talk to Charles about why PUCs matter, why they might be a rare spot for progress over the next four years, and why (and how) normal people should talk to their local public utility commissioner. Shift Key is hosted by Jesse Jenkins, a professor of energy systems engineering at Princeton University, and Robinson Meyer, Heatmap’s executive editor.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
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Here is an excerpt from our conversation:
Robinson Meyer: I want to pivot a bit and ask something that I think Jesse and I have talked about, something that you and I have talked about, Charles, is that the PUCs are going to be very important during the second Trump administration, and there’s a lot of possibilities, or there’s some possibilities for progress during the Trump administration, but there’s also some risks. So let’s start here: As you survey the state utility landscape, what are you worried about over the next four years or so? What should people be paying attention to at the PUC level?
Charle Hua: I think everything that we’re hearing around AI data centers, load growth, those are decisions that ultimately state public utility commissioners are going to make. And that’s because utilities are significantly revising their load forecasts.
Just take Georgia Power — which I know you talked about last episode at the end — which, in 2022, just two years ago, their projected load forecast for the end of the decade was about 400 megawatts. And then a year later, they increased that to 6,600 megawatts. So that’s a near 17x increase. And if you look at what happens with the 2023 Georgia Power IRP, I think the regulators were caught flat footed about just how much load would actually materialize from the data centers and what the impact on customer bills would be.
Meyer:And what’s an IRP? Can you just give us ...
Hua: Yes, sorry. So, integrated resource plan. So that’s the process by which utilities spell out how they’re proposing to make investments over a long term planning horizon, generally anywhere from 15 to 30 years. And if we look at, again, last year’s integrated resource plan in Georgia, there was significant proposed new fossil fuel infrastructure that was ultimately fully approved by the public service commission.
And there’s real questions about how consumer interests are or aren’t protected with decisions like that — in part because, if we look at what’s actually driving things like rising utility bills, which is a huge problem. I mean, one in three Americans can’t pay their utility bills, which have increased 20% over the last two years, two to three years. One of the biggest drivers of that is volatile gas prices that are exposed to international markets. And there’s real concern that if states are doubling down on gas investments and customers shoulder 100% of the risk of that gas price volatility that customers’ bills will only continue to grow.
And I think what’s going on in Georgia, for instance, is a harbinger of what’s to come nationally. In many ways, it’s the epitome of the U.S. clean energy transition, where there’s both a lot of clean energy investment that’s happening with all of the new growth in manufacturing facilities in Georgia, but if you actually peel beneath the layers and you see what’s going on internal to the state as it relates to its electricity mix, there’s a lot to be concerned about.
And the question is, are we going to have public utility commissions and regulatory bodies that can adequately protect the public interest in making these decisions going forward? And I think that’s the million dollar question.
This episode of Shift Key is sponsored by …
Download Heatmap Labs and Hydrostor’s free report to discover the crucial role of long duration energy storage in ensuring a reliable, clean future and stable grid. Learn more about Hydrostor here.
Music for Shift Key is by Adam Kromelow.